The Long Shadow of Adverse Childhood Experiences (ACEs): 1. Mental Health Outcomes in aCommunity Sample
Bibliographic record
Abstract
Background: Research has consistently linked adverse childhood experiences (ACEs) to negative mental and physical health outcomes later in life. The present study replicated the landmark ACE study in the United States, but within a Canadian adult primary care population (N=3,924). Methods: Participants completed an Adverse Childhood Experiences Questionnaire and a self-report measure for the diagnoses of a variety of mental health problems. Using age and socioeconomic status as covariates, odds ratios were calculated with multivariate logistic regression separately for males and females. Results: Approximately 30.3% of the sample had an ACE score of 0, while the scores for 1, 2, 3 and 4+ACEs were 23.1%, 16.4%, 10.5% and 12.9%, respectively. As the number of ACEs increased, the odds of either a previous or current mental health problem generally also increased in a dose-response manner. This relationship was particularly strong for the DSM-V categories of depressive disorders, anxiety disorders, obsessive-compulsive disorders, bipolar and related disorders, substance-related and addictive disorders, and neurodevelopmental disorders. Similar patterns of results were observed for both males and females. Conclusion: This research replicates results from population-based studies that have examined relationships between ACEs and mental health problems. A better understanding of the factors that underlie the risk for mental disorders is critical to develop prevention and early intervention models. The implications for ACEs screening and intervention within primary care populations are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".